面向SAR图像目标分类的CNN模型可视化方法

李妙歌; 陈渤; 王东升; 刘宏伟

doi:10.12000/JR23107

面向SAR图像目标分类的CNN模型可视化方法

DOI: 10.12000/JR23107

西安电子科技大学雷达信号处理全国重点实验室西安 710071

基金项目: 国家自然科学基金(U21B2006)，陕西省青年创新团队项目，中央高校基本科研业务费专项资金(QTZX23037, QTZX22160)，“111”计划(B18039)

详细信息

作者简介:
李妙歌，硕士生，主要研究方向为雷达目标识别、SAR图像解译、机器学习与人工智能等

陈　渤，博士，教授，博士生导师，主要研究方向为机器学习、统计信号处理、雷达目标识别与检测、深度学习网络、大规模数据处理等

王东升，博士生，主要研究方向为贝叶斯概率统计、生成模型、机器学习等

刘宏伟，博士，教授，博士生导师，主要研究方向为雷达目标识别、认知雷达、网络化协同探测、雷达智能化探测等

通讯作者:
陈渤 bchen@mail.xidian.edu.cn

责任主编：计科峰 Corresponding Editor: JI Kefeng
中图分类号: TN957.51
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出版历程
- 收稿日期: 2023-06-16
- 修回日期: 2023-09-24
- 网络出版日期: 2023-10-20
- 刊出日期: 2024-04-28

CNN Model Visualization Method for SAR Image Target Classification

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (U21B2006), Shaanxi Youth Innovation Team Project, The Fundamental Research Funds for the Central Universities (QTZX23037, QTZX22160), The 111 Project (B18039)

More Information

Corresponding author: CHEN Bo, bchen@mail.xidian.edu.cn

摘要

摘要: 卷积神经网络(CNN)在合成孔径雷达(SAR)图像目标分类任务中应用广泛。由于网络工作机理不透明，CNN模型难以满足高可靠性实际应用的要求。类激活映射方法常用于可视化CNN模型的决策区域，但现有方法主要基于通道级或空间级类激活权重，且在SAR图像数据集上的应用仍处于起步阶段。基于此，该文从神经元特征提取能力和网络决策依据两个层面出发，提出了一种面向SAR图像的CNN模型可视化方法。首先，基于神经元的激活值，对神经元在其感受野范围内的目标结构学习能力进行可视化，然后提出一种通道-空间混合的类激活映射方法，通过对SAR图像中的重要区域进行定位，为模型的决策过程提供依据。实验结果表明，该方法给出了模型在不同设置下的可解释性分析，有效拓展了卷积神经网络在SAR图像上的可视化应用。
- 合成孔径雷达 /
- 可视化分析 /
- 卷积神经网络 /
- 类激活映射 /
- 神经元
Abstract: Convolutional Neural Network (CNN) is widely used for image target classifications in Synthetic Aperture Radar (SAR), but the lack of mechanism transparency prevents it from meeting the practical application requirements, such as high reliability and trustworthiness. The Class Activation Mapping (CAM) method is often used to visualize the decision region of the CNN model. However, existing methods are primarily based on either channel-level or space-level class activation weights, and their research progress is still in its infancy regarding more complex SAR image datasets. Based on this, this paper proposes a CNN model visualization method for SAR images, considering the feature extraction ability of neurons and their current network decisions. Initially, neuronal activation values are used to visualize the capability of neurons to learn a target structure in its corresponding receptive field. Further, a novel CAM-based method combined with channel-wise and spatial-wise weights is proposed, which can provide the foundation for the decision-making process of the trained CNN models by detecting the crucial areas in SAR images. Experimental results showed that this method provides interpretability analysis of the model under different settings and effectively expands the application of CNNs for SAR image visualization.
- Synthetic Aperture Radar (SAR) /
- Visualization /
- Convolutional Neural Network (CNN) /
- Class Activation Mapping (CAM) /
- Neurons

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图 1 面向SAR图像的CNN模型可视化方法框图

Figure 1. CNN model visualization method for SAR images

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图 2 CS-CAM算法流程图

Figure 2. Pipeline of CS-CAM

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图 3 SAR图像神经元可视化结果图

Figure 3. Visualization of neurons at different network layers

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图 4 第12层TOP-9神经元可视化结果图

Figure 4. Visualization of layer 12 TOP-9 neurons

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图 5 第12层TOP-9神经元归一化个数统计图

Figure 5. Layer 12 TOP-9 neuron normalization statistics chart

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图 6 各类激活映射方法可视化结果图

Figure 6. CAM-based methods’ visualization of the same input image

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图 7 输入图像

Figure 7. Input images

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图 8 各类激活映射方法针对CNN不同层的可视化结果

Figure 8. Visualization of CAM-based methods in each stage of CNN

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图 9 VGG-16网络各Stage最后一个卷积层内各通道特征图的方差变化图

Figure 9. The variance statistics of the feature maps for each channel in the last convolutional layer of each Stage in VGG-16

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图 10 CS-CAM Sanity Check结果图

Figure 10. Sanity Check results of CS-CAM

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图 11 不同网络准确率下类激活热力图

Figure 11. Heat maps with different network accuracy

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图 12 不同初始化方式下类激活热力图

Figure 12. Heat maps under different initialization modes

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图 13 同类输入类激活热力图

Figure 13. Heat maps of inputs with same class

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图 14 异类输入激活热力图

Figure 14. Heat maps of inputs with different classes

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表 1 不同类激活映射方法类激活权重计算表

Table 1. Class activation weights for different CAM-based methods

类激活方法名称	类激活权重计算公式
Grad-CAM^[31]	$a_{j\_{\rm{channel} } }^c = \dfrac{1}{Z}\displaystyle\sum\limits_x {\displaystyle\sum\limits_y {\frac{ {\partial {S^c} } }{ {\partial {\boldsymbol{h} }_j^{(L)}(x,y)} } } }$
Grad-CAM++^[32]	${a}_{j\_{\rm{channel} } }^{c}={\displaystyle \sum _{x}{\displaystyle \sum _{y}\frac{\dfrac{ {\partial }^{2}{S}^{c} }{ {\left(\partial { {\boldsymbol{h} } }_{j}^{(L)}(x,y)\right)}^{2} } }{2\dfrac{ {\partial }^{2}{S}^{c} }{ {\left(\partial { {\boldsymbol{h} } }_{j}^{(L)}(x,y)\right)}^{2} }+{\displaystyle \sum _{x}{\displaystyle \sum _{y}{ {\boldsymbol{h} } }_{j}^{(L)}(x,y)\left\{\frac{ {\partial }^{3}{S}^{c} }{ {\left(\partial { {\boldsymbol{h} } }_{j}^{(L)}(x,y)\right)}^{3} }\right\} } } }\cdot{\rm{ReLU} }\left(\frac{\partial {S}^{c} }{\partial { {\boldsymbol{h} } }_{j}^{(L)}(x,y)}\right)} }$
Ablation-CAM^[33]	$a_{j\_{\rm{channel} } }^c = \dfrac{ { {S^c} - S_j^c} }{ { {S^c} } }$
Score-CAM^[34]	$a_{j\_{\rm{channel} } }^c = \dfrac{ {\exp\left( {\Delta S_j^c} \right)} }{ {\displaystyle\sum\limits_j {\exp\left( {\Delta S_j^c} \right)} } }$
Eigen-CAM^[35]	${ {\boldsymbol{h} }^{(L)} } = {\boldsymbol{U\varSigma} } { {\boldsymbol{V} }^{\rm{T} } } ,{a_{j\_{\rm{channel} } }^c = {{\boldsymbol{v}}_1} }$
Eigengrad-CAM	${ {\boldsymbol{h} } }^{(L)}\cdot \dfrac{1}{Z}{\displaystyle \sum _{x}{\displaystyle \sum _{y}\frac{\partial {S}^{c} }{\partial { {\boldsymbol{h} } }_{j}^{(L)}(x,y)} } }={\boldsymbol{U\varSigma} } { {\boldsymbol{V} } }^{ {\rm{T} } } , {a}_{j\_{\rm{channel} } }^{c} ={{\boldsymbol{v}}}_{1}$
Layer-CAM^[36]	$a_j^c{\left( {x,y} \right)_{\_{\rm{spatial} } } } = \dfrac{ {\partial {S^c} } }{ {\partial {\boldsymbol{h} }_j^{(L)}(x,y)} }$

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表 2 MSTAR数据集

Table 2. MSTAR dataset

目标名称	目标类型	训练集(17°俯仰角)	测试集(15°俯仰角)
2S1	自行榴弹炮	299	274
BMP2	步兵战车	232	196
BRDM2	装甲侦察车	298	274
BTR60	装甲侦察车	233	196
BTR70	装甲侦察车	256	195
T62	坦克	299	273
T72	坦克	232	196
ZIL131	军用卡车	299	274
ZSU234	自行高炮	299	274
D7	推土机	299	274

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表 3 MSTAR数据集模型训练结果(%)

Table 3. Model training results (%)

网络模型	训练集识别率	测试集识别率
VGG-16 (RI)	98.7	94.1
VGG-16 (PT)	99.7	98.0

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表 4 定量分析指标结果表

Table 4. Quantitative analysis results

方法	评估指标
方法	Average Increase (%)	Average Drop (%)	Infidelity	Max-sensitivity
Grad-CAM	13.6	86.4	2.425	0.752
Grad-CAM++	13.6	64.0	2.987	0.612
Ablation-CAM	4.5	62.1	0.698	0.447
Layer-CAM	13.6	63.1	0.609	0.450
Eigengrad-CAM	13.6	77.8	0.619	0.459
Eigen-CAM	13.6	60.3	0.452	0.452
Score-CAM	13.6	58.6	0.818	0.486
CS-CAM	13.6	57.3	0.537	0.442

下载: 导出CSV

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